As a columnar in-memory format, Apache Arrow has seen increased interest from the data analytics community. Fletcher is a framework that generates hardware interfaces based on this format, to be used in FPGA accelerators. This allows efficient integration of FPGA accelerators with various high-level software languages, while providing an easy-to-use hardware interface for the FPGA developer. The abstract descriptions of data sets stored in the Arrow format, that form the input of the interface generation step, can be complex. To generate efficient interfaces from it is challenging. In this paper, we introduce the hardware components of Fletcher that help solve this challenge. These components allow FPGA developers to express access to complex Arrow data records through row indices of tabular data sets, rather than through byte addresses. The data records are delivered as streams of the same abstract types as found in the data set, rather than as memory bus words. The generated interfaces allow for full system bandwidth to be utilized and have a low area profile. All components are open sourced and available for other researchers and developers to use in their projects.

JSON is a popular data interchange format for many web, cloud, and IoT systems due to its simplicity, human readability, and widespread support. However, applications must first parse and convert the data to a native in-memory format before being able to perform useful computations. Many big data applications with high performance requirements convert JSON data to Apache Arrow RecordBatches, the latter being a widelyused columnar in-memory format for large tabular data sets used in data analytics. In this paper, we analyze the performance characteristics of such applications and show that JSON parsing represents a bottleneck in the system. Various strategies are explored to speed up JSON parsing on CPU and GPU as much as possible. Due to performance limitation of the CPU and GPU implementations, we furthermore present an FPGA accelerated implementation. We explain how hardware components that can parse variable-sized and nested structures can be combined to produce JSON parsers for any type of JSON document. Several fully integrated FPGA-accelerated JSON parser implementations are presented using the Intel Arria 10 GX and Xilinx VU37P devices, and compared to the performance of their respective host systems; an Intel Xeon and an IBM POWER9 system. Result show the accelerators achieve an end-to-end throughput close to 7 GB/s with the Arria 10 GX using PCIe, and close to 20 GB/s with the VU37P using OpenCAPI 3. Depending on the complexity of the JSON data to parse, the bandwidth is limited by the hostto-accelerator interface or available FPGA resources. Overall, this provides a throughput increase of up to 6x, compared to the baseline application. Also, we observe a full system energy efficiency improvement of up to 59x more JSON data parsed per joule.

Streaming dataflow designs describe hardware by connecting components through streams that transport data structures. We introduce a stream-oriented specification and type system that provides a clear and intuitive way to map complex, dynamically-sized data structures onto hardware streams. This helps designers to lift the abstraction of streaming dataflow designs, reducing the design effort. The type system allows complex data structures to be as easy to use in streaming dataflow designs as in modern software languages today.

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As big data analytics systems are squeezing out the last bits of performance of CPUs and GPUs, the next near-term and widely available alternative industry is considering for higher performance in the data center and cloud is the FPGA accelerator. We discuss several challenges a developer has to face when designing and integrating FPGA accelerators for big data analytics pipelines. On the software side, we observe complex run-time systems, hardware-unfriendly in-memory layouts of data sets, and (de)serialization overhead. On the hardware side, we observe a relative lack of platform-agnostic open-source tooling, a high design effort for data structure-specific interfaces, and a high design effort for infrastructure. The open source Fletcher framework addresses these challenges. It is built on top of Apache Arrow, which provides a common, hardware-friendly in-memory format to allow zero-copy communication of large tabular data, preventing (de)serialization overhead. Fletcher adds FPGA accelerators to the list of over eleven supported software languages. To deal with the hardware challenges, we present Arrow-specific components, providing easy-to-use, high-performance interfaces to accelerated kernels. The components are combined based on a generic architecture that is specialized according to the application through an extensive infrastructure generation framework that is presented in this article. All generated hardware is vendor-agnostic, and software drivers add a platform-agnostic layer, allowing users to create portable implementations.